New results on the performance of distributed Bayesian detection systems

Abstract

The purpose of decision fusion in a distributed detection system is to achieve a performance that is better than that of local detectors (or sensors). We consider a distributed Bayesian detection system consisting of n sensors and a fusion center, in which the decision rules of the sensors have been given and the decisions of different sensors are conditionally independent. We assume that the decision rules of the sensors can be optimum or suboptimum, and that the probabilities of detection and false alarm of the sensors can be different. Theoretical analysis on the performance of this fusion system is carried out. Conditions for the fusion system to achieve a global risk that is smaller than local risks are obtained.